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Award in Introduction to Artificial Intelligence

Introduction to Artificial Intelligence (AI) module covers the fundamental concepts, theories, and techniques of artificial intelligence.

MQF/EQF Level: 7 ECTS : 10 Credits 0 Enrolled
  • Director’s Introduction
    03:27
    Orientation
    10:52
    Understanding Podcasts
    01:46
    Understanding Pre-recorded Lectures
    02:25
    Understanding Pre-recorded Interviews
    01:54
    Understanding Case Studies
    01:47
    Understanding Discussion Boards
    02:30
    Ascencia Style Sheet
    AI Policy
    Policy for Online Interaction and Behaviour for E-Academic Students
  • Introductory Podcast
    00:08:06
  • Podium Lecture: Thinking Machines, Birth of AI, Philosophies and 4 Pillars of Artificial Intelligence
    00:36:21
    Lecture: Part 1: AI and Machine Learning Tools
    00:38:00
    Lecture: Part 2: AI and Machine Learning Tools
    00:47:37
    Lecture: Part 3: AI and Machine Learning Tools
    00:40:51
    Podium Lecture: The Past, Present and Future of AI
    00:19:19
    Guest Interview: Nikola Markovikj
    00:48:34
    Lecture 1: Part 1: AI, Blockchain, and Automation in Project Management
    01:02:12
    Lecture 1: Part 2: AI, Blockchain, and Automation in Project Management
    01:04:42
    Lecture 2: AI and Data Analytics in Project Management
    01:22:28
  • Podium Lecture: Introducing Heuristics to a Search Problem, Greedy, A*, Properties of a Heuristic
    00:35:49
    Podium Lecture: Introducing Logic and Knowledge to a Problem, Toy Scenarios, Basic Knowledge Based Agents
    00:40:10
    Podium Lecture: Using Propositional Logic to Inform the Agent, Entailment and Model Checking
    00:32:18
    Podium Lecture: Extending Propositional Logic Using First Order Logic
    00:45:41
  • Lecture 1: Part 1: Introduction to Artificial Intelligence
    00:42:31
    Lecture 1: Part 2: Introduction to Artificial Intelligence
    00:56:27
    Podium Lecture: Introducing Agents that act Under Uncertain Conditions, Elements of Probabilistic Reasoning
    00:33:22
  • Lecture 2: Introduction to AI and Machine Learning
    01:32:54
    Screen Recording: 2.1: Part 1: IntroMLAI_CutNeuralNetwork
    01:37:14
    Screen Recording 2.1: Part 2: IntroMLAI_Network
    00:42:03
    Screen Recording 2.1: TensorflowPlayground
    00:19:12
  • Lecture 3: Data Engineering in Machine Learning
    01:03:00
    Screen Recording 3.1: Data Engineering
    00:38:30
  • Lecture 4: Part 1: Supervised Learning Foundations
    00:31:40
    Lecture 4: Part 2: Supervised Learning Foundations
    00:36:23
    Lecture 4.2: Logistic Regression
    01:06:15
    Screen Recording 4.2: Logistic Regression
    01:16:49
    Lecture 4.3: Gradient Descent
    00:59:22
    Screen Recording 4.3: Gradient Descent
    00:40:01
    Lecture 4.4: KNN
    00:30:00
    Screen Recording 4.4: KNN
    00:32:02
    Lecture 4.5: SVM
    01:24:38
    Screen Recording 4.5.1: SVM
    00:16:23
    Screen Recording 4.5.2: SVM
    00:14:50
    Screen Recording 4.5.3: SVM
    00:18:26
    Screen Recording 4.5.4: SVM
    00:12:18
    Lecture 4.6: Decision Trees
    00:48:03
    Screen Recording 4.7.1: Ensemble
    00:22:20
    Screen Recording 4.7.2: Ensemble
    00:08:16
  • Lecture 5: Neural Networks Foundations
    00:55:02
    Lecture 5.1: KMeans
    00:39:56
    Screen Recording 5.1: KMeans
    00:32:03
    Lecture 5.2: DBSCAN
    00:33:10
    Screen Recording 5.2: DBSCAN
    00:29:54
    Lecture 5.3: Agglomerative
    00:18:03
    Screen Recording 5.3: Agglomerative
    00:19:56

Introduction to Artificial Intelligence (AI) module covers the fundamental concepts, theories, and techniques of artificial intelligence. The module covers a broad overview of AI, including its history, current state, and future potential and philosophical dilemmas. Students will learn about the different subfields of AI, including machine learning, computer vision, robotics, and natural language processing, and will be introduced to the basic concepts of artificial intelligence, such as decision trees, neural networks, and fuzzy logic.


Programme Outcomes

  • Develop an Understanding of AI fundamentals: Students will gain a comprehensive understanding of the core theories of AI, including machine learning, logical reasoning as well as reasoning in uncertainty.
  • Knowledge of AI algorithms: Students will learn about the different AI algorithms and techniques, such as decision trees, neural networks, and fuzzy logic, and will understand how to implement and apply these algorithms in the context of logical reasoning an uncertain environment.
  • Critical analysis of AI: Students will develop critical analysis skills by examining the implications of AI from multiple perspectives, including those of ethics, philosophy, and technology.
  • Develop an understanding of the ethical and moral implications of AI: Students will learn about the ethical and moral implications of AI, including issues related to privacy, autonomy, and the distribution of benefits and harms.

  • A Bachelor's degree or post-graduate degree in business, administration, management, or a related field
  • Minimum 180 ECTS previously acquired at a Higher Education institution
  • 2 years of work experience
  • A GPA score of 2.5 (C+) or higher (or equivalent) will be required
  • Proof of C1 level of English or equivalent

NOTE: Individuals may apply and ask for consideration to be given to considerable high level leadership experience in lieu of some of typical academic requirements

Course Syllabus Podcast

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  • Multiple choice assessment - 30%
  • Assignment - 70%
  • E-portfolio
  • You can submit your assessment at any time during the course.


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What’s included

  • Duration :  
    Full Time - 3 Months
    Part Time - 6 Months
  • Learning: 250 +Hours
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